The terms generalization and overfitting are well researched within the neural network learning paradigm. As such neural network writers can employ a wide variety of tools to ensure that their networks maximize generalization whilst minimizing overfitting. These terms have never fully been explored within the traditional artificial immune system learning paradigm. This study intends to address this by providing a clear unambiguous definition of generalization and overfitting in terms of artificial immune systems and the shape space model introduced by Perelson. Using these definitions a metric is suggested to measure the performance of a population of detectors. Various detector generating algorithms and matching rules are scrutinized in terms of their generalization and overfitting abilities and number of new algorithms and improvements are suggested to overcome the constraints imposed by the old algorithms and matching rules.